[2602.02356] NAB: Neural Adaptive Binning for Sparse-View CT reconstruction

Computer Science > Computer Vision and Pattern Recognition arXiv:2602.02356 (cs) [Submitted on 2 Feb 2026 (v1), last revised 2 Mar 2026 (this version, v2)] Title:NAB: Neural Adaptive Binning for Sparse-View CT reconstruction Authors:Wangduo Xie, Matthew B. Blaschko View a PDF of the paper titled NAB: Neural Adaptive Binning for Sparse-View CT reconstruction, by Wangduo Xie and 1 other authors View PDF HTML (experimental) Abstract:Computed Tomography (CT) plays a vital role in inspecting the internal structures of industrial objects. Furthermore, achieving high-quality CT reconstruction from sparse views is essential for reducing production costs. While classic implicit neural networks have shown promising results for sparse reconstruction, they are unable to leverage shape priors of objects. Motivated by the observation that numerous industrial objects exhibit rectangular structures, we propose a novel Neural Adaptive Binning (NAB) method that effectively integrates rectangular priors into the reconstruction process. Specifically, our approach first maps coordinate space into a binned vector space. This mapping relies on an innovative binning mechanism based on differences between shifted hyperbolic tangent functions, with our extension enabling rotations around the input-plane normal vector. The resulting representations are then processed by a neural network to predict CT attenuation coefficients. This design enables end-to-end optimization of the encoding parameters -- ...